Models, methods, and the machinery of intelligence.
arXiv
Verdict: NotableAs the production of software passes into the hands of software itself, coding agents are increasingly commissioned to build agents fitted to their users' own workflows.
Multimodal language models (MLLMs) exhibit promising spatial reasoning capabilities, yet the internal mechanisms supporting them remain poorly understood.
Terminal agents are moving beyond local operations toward completing real-world professional tasks.
Continual learning requires models to accumulate past experience and transfer it to new tasks.
Recursive self-improvement (RSI) aims to transform interaction experience into persistent improvements, yet external feedback is costly and world-model predictions may become unreliable ....
Continual post-training faces a dual challenge of acquisition and retention: models must learn new capabilities while preserving prior knowledge.
Computer-use agents can perform increasingly complex GUI tasks, yet unfamiliar software and workflows requiring human domain knowledge remain challenging.
Coding agents are increasingly used to build programs from scratch as software engineers do rather than to resolve issues within a repository or to generate a repository from a given ....
Attention Residuals (AttnRes) enables content-dependent retrieval over network depth, which can be viewed as single-stream softmax attention along the depth axis.